The University of California, Los Angeles (UCLA) is one of the leading universities in America. It is considered a breeding ground for people who excel in academic, research, and athletics programs. Now, the Westwood-based research university aims to contribute to the billion-dollar healthcare industry. Researchers at UCLA have a bold idea of incorporating machine learning to medical purposes. The concept is not entirely new; however, the process that they are using caters to specific patients.

The Westwood-based research university developed a study about a new algorithm called the Tree of Predictors (ToPs). It is capable of predicting the survival rates of heart failure patients. ToPs could also determine how long a patient with this condition will live. UCLA scientists believe that the new algorithm is going to help a physician’s decision-making on dealing with patients who are waiting for a heart transplant.

Impact of ToPs

According to reports, ToPs make use of 53 data points such as gender, blood type, age, and body mass index. These points evaluate patients who are waiting for a heart transplant. Thirty-three of the data points focus on the recipient, while 14 of the data points center to the donor. The remaining six data points are for the compatibility of the recipient and the donor.

Mihaela van der Schaar is the Chancellor’s Professor of Electrical and Computer Engineering at the UCLA Samueli School of Engineering. He said, “Our work suggests that more lives could be saved with the application of this new machine learning-based algorithm. It would be especially useful for determining which patients need heart transplants most urgently and which patients are good candidates for bridge therapies such as implanted mechanical-assist devices.”

Scientists believe that ToPs could produce accurate results. They successfully tested it on the healthcare data, which were gathered for almost 30 years. The data were from patients who have records with the United Network for Organ Sharing.

In addition, ToPs can absorb new information over time because it is powered by machine learning.

ToPs Having Better Accuracy Over Other Methods

ToPs have already made a mark in the healthcare industry even though it is just a new method. Further test results show that ToPs bested some predictions from other machine learning systems. UCLA’s algorithm got a 14% accuracy in verifying the survival rates of heart failure patients.

Accuracy of ToPs Prediction Models

Just when people think that ToPs only do one thing, they are wrong. UCLA scientists made sure that the new algorithm is not a one trick pony. ToPs could evaluate possible risk setups for probable transplant candidates. Not only that because the new algorithm could also integrate more information as the treatment for the patient progresses.

Another impressive thing that ToPs do is they also cater to other industries. The new algorithm has the capability to forecast incidents of credit card fraud. To date, researchers are trying to look for ways on how to associate ToPs into the process of heart donation decision making.

UCLA researchers only have one thing in mind and that is to help the field of medicine propel into the future. Through worthwhile studies like ToPs, they could surely do that. The new algorithm is going to create a bold impact, especially for doctors and their patients who are in need of heart transplants.